DİNLE

MSc. Thesis Defense:Mehmet Emin Mumcuoğlu11-07-2019

Driving Behavior Classification for Heavy-Duty Vehicles Using LSTM Networks

 

 

Mehmet Emin Mumcuoğlu
Mechatronics, M.Sc. Thesis, 2019

 

Thesis Jury

Prof. Dr. Mustafa Ünel (Thesis Advisor), Asst. Prof. Hüseyin Özkan,

Asst. Prof. Ertuğrul Çetinsoy

 

 

Date & Time: July 16th, 2019 – 11:30

Place: FENS G029

Keywords: Driving Behavior, Driver Classification, Acceleration Behavior, Car Following Behavior, Road Design, Heavy-Duty Vehicles, LSTM Classifier

 

Abstract

 

Despite growing autonomous driving trend, human is still a major factor in the current vehicle technology. Drivers have a great impact on both fuel economy and accident prevention. Therefore, identification and evaluation of driving behaviors are crucial to improve the performance, safety and energy management of vehicle technologies, particularly for heavy-duty vehicles. In this thesis, several driving behaviors with different acceleration and car following characteristics are generated on a realistic truck model in IPG’s TruckMaker simulation environment. A Long Short Term Memory (LSTM) classifier is then utilized to recognize driving behaviors. First, six drivers are defined based on their longitudinal and lateral acceleration limits. The classifier is trained using driving signals acquired from the simulated truck which follows an artificial training road with different trailer loads. The training road is designed to cover possible road curves that can be seen in highways. The model is tested with driving signals that are collected from a realistic road using the same method. Then, three drivers (calm, normal and aggressive) are defined based on their longitudinal acceleration profiles in car following and the classifier is trained and tested using driving signals of these drivers in different traffic scenarios. Results show that the proposed LSTM classifier is capable of successfully capturing the dynamic relations encoded in driving signals and recognizing different driving behaviors in small time samples.